Liquid cooling pipeline temperature control system based on liquid flow regulation

Abstract

The invention discloses a liquid cooling pipeline temperature control system based on liquid flow regulation, and the system comprises a data collection module which is used for collecting the temperature, internal resistance and current of a battery cell in real time, and carrying out the calculation to obtain the heat generated by the battery cell; the region division module is used for obtaining weight coefficients of a plurality of battery cells according to the average temperature of the battery module and the heat production of the battery cells, and performing region division according to the weight coefficients and the space coordinates of the battery cells by utilizing a clustering algorithm; the optimization control module is used for constructing a region optimization model according to the target temperature and solving flow set values of a plurality of regions based on constraint conditions; and the flow feedback module is used for calculating the pressure compensation flow according to the pipeline shapes of the plurality of areas and the current flow, calculating the aging compensation flow according to the internal resistance of the battery cell, and updating the flow set value again by using the pressure compensation flow and the aging compensation flow. The invention relates to the technical field of battery thermal management, and solves the technical problems of non-uniform temperature adjustment and inaccurate control of an existing liquid cooling temperature control system.

Description

Technical Field

[0001] The present invention belongs to the field of battery thermal management, relates to temperature control technology, and specifically is a liquid-cooled pipeline temperature control system based on liquid flow regulation. Background Art

[0002] With the rapid development of new energy vehicles and energy storage systems, the thermal management of lithium-ion batteries has become a core issue affecting system safety, lifespan, and energy efficiency. Traditional liquid-cooled temperature control systems generally adopt a global uniform flow control strategy to achieve temperature control by uniformly regulating the coolant flow rate, and there will be many problems in actual applications. Due to the spatial heterogeneity of heat generation in the battery modules, local hotspots are easily formed under high-rate charge and discharge conditions, and the uniform flow mode cannot implement differential heat dissipation for high-heat areas, resulting in a temperature difference inside the module exceeding the safety threshold and accelerating the battery performance degradation. At the same time, most of the existing systems perform feedback control based on static temperature thresholds, lacking the predictive response ability to dynamic working conditions (such as sudden changes in ambient temperature and transient large-current impacts), and there is a lag in temperature regulation, making it difficult to maintain thermal stability.

[0003] At the system architecture level, traditional solutions often ignore the influence of pipeline topology on flow distribution. Local resistance factors such as elbows and roughness in complex pipeline systems easily cause pressure drop imbalance, resulting in a deviation between the theoretical flow rate and the actual cooling effect, further exacerbating the temperature non-uniformity phenomenon. In addition, during the long-term cyclic use of the battery pack, the progressive aging of the internal resistance of the battery cells will change the heat generation characteristics, and the existing systems lack an adaptive compensation mechanism based on the state of health, leading to a mismatch between the cooling strategy and the actual requirements of the battery cells and accelerating the capacity degradation.

[0004] Moreover, existing liquid-cooled systems often adopt a high-flow redundancy design to pursue temperature control effects, which not only increases the pumping energy consumption, but also the frequent stepwise flow regulation will cause hydraulic oscillation, threatening the pipeline sealing performance and the valve lifespan. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes a liquid-cooled pipeline temperature control system based on liquid flow regulation to solve the technical problems of uneven temperature regulation and inaccurate control in existing liquid-cooled control systems.

[0006] To achieve the above object, the first aspect of the present invention provides a liquid-cooled pipeline temperature control system based on liquid flow regulation, including:

[0007] A data acquisition module: used to collect the cell temperature, cell internal resistance, and current in real time, and calculate the heat generation of the cells;

[0008] Region division module: used to obtain the weight coefficients of a number of battery cells based on the average temperature of the battery module and the heat generation of the battery cells, and use the clustering algorithm to perform region division according to the weight coefficients and the spatial coordinates of the battery cells to obtain a number of regions;

[0009] Optimization control module: used to construct a region optimization model according to the target temperature, solve the flow rate setting values of a number of regions based on the constraint conditions, and control the liquid cooling valve using the flow rate setting values;

[0010] Flow feedback module: used to calculate the pressure compensation flow rate according to the pipeline shape and the current flow rate of a number of regions, calculate the aging compensation flow rate according to the internal resistance of the battery cells, and use the pressure compensation flow rate and the aging compensation flow rate to update the flow rate setting value again.

[0011] Further, the calculation formula for the heat generation of the battery cells is: Q heat,i =I 2 R i +T i ΔS i ; where Q heat,i represents the heat generation of the i-th battery cell, I represents the current, R i represents the internal resistance of the i-th battery cell, T i represents the temperature of the i-th battery cell, ΔS i represents the entropy change coefficient of the i-th battery cell, which is obtained by measuring with differential scanning calorimetry (DSC) of electrochemistry micro differential calorimetry.

[0012] By introducing the electrochemical heat effect through the entropy change coefficient, it is applicable to the thermal management requirements under dynamic working conditions and different temperature environments, and can more comprehensively and accurately reflect the actual heat generation characteristics of the battery cells.

[0013] Further, the obtaining of the weight coefficients of a number of battery cells based on the average temperature of the battery module and the heat generation of the battery cells includes:

[0014] Calculate the average temperature T avg of the battery cell module in real time;

[0015] According to the formula calculate the weight coefficient w i of the i-th battery cell; where ΔT max represents the maximum allowable temperature difference of the battery cell module, α1 represents the temperature deviation weight, α2 represents the heat load ratio weight, and α1 + α2 = 1.

[0016] Further, the performing of region division according to the weight coefficients and the spatial coordinates of the battery cells using the clustering algorithm includes:

[0017] According to the CAD drawing of the battery module or the installation layout drawing of the battery module, obtain the spatial coordinates of the battery cells to get the spatial coordinates of the battery cells (x i , yi );

[0018] Generate an adjacency list based on the physical layout of the battery cells and define the relationship between adjacent battery cells;

[0019] Calculate the similarity Sim(i,j) of the relationship between adjacent battery cells to obtain a similarity matrix S. The calculation formula is: Sim(i,j) = 1 - |w i - w j | / (w max - w min ); where w max and w min represent the maximum and minimum values of the weight coefficients respectively;

[0020] Calculate the Laplacian matrix according to the similarity matrix: L = D - S; where D represents the degree matrix, and D ii = ∑ j S ij , S ij = Sim(i,j);

[0021] Perform eigen - decomposition on the Laplacian matrix L and take the eigen - vectors corresponding to the first k smallest eigenvalues to obtain a number of eigen - vectors;

[0022] Cluster a number of eigen - vectors based on the K - means algorithm to obtain a number of regions.

[0023] During the calculation of the weight coefficients, adding the heat generation of the battery cells and the degree of temperature deviation can make the region division result focus on the battery cells with high heat generation or abnormal temperature preferentially. And through the eigen - decomposition of the Laplacian matrix and the K - means clustering algorithm, divide the regions according to the spatial coordinates to form regions that are physically adjacent and have similar thermal characteristics. This can not only reduce the temperature difference between regions in subsequent liquid - cooling control but also optimize the distribution efficiency of cooling resources and improve the intelligence of temperature control.

[0024] Furthermore, the construction of the region optimization model according to the target temperature includes:

[0025] Calculate the average temperature of the battery cells in region j and calculate the temperature weight w and the flow weight w T,j and flow weight w Q,j ; where C j represents region j, L j represents the total pipeline length of region j, L max represents the maximum value of the pipeline lengths in all regions, and λ represents the global flow fluctuation penalty coefficient, which is obtained through experimental calibration;

[0026] Determine the set of adjacent regions Ν(j) of region j according to the pipeline topological connection relationship;

[0027] Use a sliding event window to statistically calculate the average flow rate of area j within a historical preset time period to obtain the historical average flow rate Q j,avg ;

[0028] Construct the area optimization model as:

[0029] ;

[0030] where, T target represents the target temperature, represents the average cell temperature of the adjacent area k, η represents the temperature balance coefficient between areas, which is obtained through experimental calibration, represents the flow rate setting values of several areas, which are the solution results of the optimization model, t represents time, and H represents the preset period.

[0031] In the area optimization model, the first term is the temperature deviation term, which is used to make the temperature of each area converge to the target value. The second term is the flow rate fluctuation penalty term, which is used to suppress the adjustment of frequent flow rates and reduce the oscillation risk of the system. The third term is the temperature difference balance term between areas, which is related to the adjacent areas through the thermal resistance model R in the prediction model jk to prevent the temperature difference between adjacent areas from being too large and improve the overall temperature uniformity.

[0032] Furthermore, solving the flow rate setting values of several areas based on the constraint conditions includes:

[0033] Construct a prediction model based on the model predictive control theory;

[0034] where, Q cool,j (t) represents the area cooling power, M j represents the total mass of the area cells, c p,j represents the area equivalent specific heat capacity, R jk represents the thermal resistance between area j and the adjacent area k, and Δt represents the time difference;

[0035] Use the prediction model to predict the average temperature change of each area within the preset period H to obtain the average predicted temperature of area j

[0036] Input the average predicted temperature of area j into the area optimization model, and solve the flow rate setting values of several areas according to the constraint conditions where, the constraint conditions include:

[0037] Area flow rate constraint: Q j,min ≤Q j ≤Q j,max ; where, Q j,min 、Q j,maxrespectively represent the minimum and maximum allowable flow rates of the pipeline in area j;

[0038] Global total flow rate constraint: where Q pump_max represents the maximum output flow rate of the pump;

[0039] Adjacent area flow rate difference constraint: |Q j - Q k | ≤ ΔQ max ; where ΔQ max represents the maximum allowable flow rate difference between adjacent areas.

[0040] In the process of solving the flow rate set value, the temperature change is dynamically predicted through model predictive control (MPC) to guide the system to adjust the flow rate in advance; and combined with the flow rate constraints (minimum / maximum flow rate, global total flow rate, adjacent area flow rate difference), the optimization result can not only meet the thermal management requirements, but also ensure the safe operation of the system under physical limitations, avoiding overload or excessive local pressure.

[0041] Furthermore, the use of the flow rate set value to control the liquid cooling valve includes:

[0042] Construct the PID control equation as: where K P,j , K I,j , K D,j respectively represent the proportional coefficient, integral coefficient and differential coefficient of area j, e j (t) represents the area flow rate deviation, and Q j (t) represents the real-time flow rate value of area j, which is obtained through a flow rate sensor;

[0043] Use the PID control equation to control the valve opening A j (t).

[0044] Furthermore, the calculation of the pressure compensation flow rate according to the pipeline shapes of several areas and the current flow rate includes:

[0045] Set the area pressure drop correction coefficient α j according to the pipeline elbows and roughness of several areas;

[0046] Calculate the equivalent cross-sectional area S j of the area valve according to the valve opening A j , and the calculation formula is: S j = A jmax × (A j (t) / 100); where A jmax represents the maximum cross-sectional area when the valve is fully open;

[0047] According to the formula Calculate the pressure compensation flow rate ΔQ P,j ; where ρ represents the density of the coolant, D h,j represents the hydraulic diameter of region j, and ΔP j represents the pressure loss, and ΔP d represents the upper limit of the allowable pressure of the system.

[0048] Calculate the pressure drop correction coefficient α based on the pipeline shape and hydraulic diameter, which can compensate for the flow loss caused by the pipeline structure in real time and avoid affecting the cooling effect due to excessive local resistance.

[0049] Furthermore, calculating the aging compensation flow rate according to the internal resistance of the battery cell includes:

[0050] Statistical average internal resistance of the battery cells in region j and the initial average internal resistance R of the battery cells j,new ;

[0051] According to the formula Calculate the aging compensation flow rate ΔQ R,j ; where γ j represents the aging compensation intensity coefficient of region j, which is obtained through experimental calibration, and Q j,base represents the basic flow rate setting value of region j.

[0052] Utilize the change of the internal resistance R of the battery cell reflecting the battery aging i to dynamically adjust the flow rate to ensure that the aging battery cells can still obtain sufficient cooling capacity; combined with the pressure compensation mechanism, it jointly improves the adaptability of the system to complex working conditions and long-term use.

[0053] Furthermore, the method of using the pressure compensation flow rate and the aging compensation flow rate to re-update the flow rate setting value includes:

[0054] Sum the flow rate setting value of region j, the pressure compensation flow rate ΔQ P,j and the aging compensation flow rate ΔQ R,j to obtain the updated flow rate;

[0055] Take the minimum value of the updated flow rate and the maximum allowable flow rate Q of region j j,max as the updated final setting value.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] In terms of the accuracy of temperature control, based on the real-time temperature of the battery cells, heat generation characteristics, and spatial layout, the heat dissipation areas are dynamically divided and the flow rates of each area are differentially adjusted, effectively reducing the temperature difference inside the battery module and avoiding problems such as local overheating or uneven cooling. At the same time, combined with the prediction model and real-time feedback mechanism, it can quickly respond to dynamic working conditions changes, such as high-load charging and discharging or sudden changes in ambient temperature, ensuring that the temperature is always stable within a safe range; moreover, the introduction of multiple constraint conditions further enhances the system stability. By restricting the flow rate adjustment boundary and balancing the temperature difference between adjacent areas, the risk of thermal runaway is suppressed. With the adaptive compensation mechanism, the interference caused by the change in pipeline resistance and battery cell aging is reduced, ensuring the robustness of long-term operation;

[0058] In terms of energy conservation and long-term management, efficient utilization of resources is achieved through global flow optimization and intelligent compensation strategies; the cooling flow rate is dynamically allocated according to pipeline characteristics and battery cell status, giving priority to meeting the needs of key areas, reducing ineffective energy consumption, lowering the power consumption of the pump and extending the valve life. Regarding the problem of battery cell aging, the internal resistance monitoring and adaptive compensation mechanism can automatically adjust the heat dissipation strategy, delay performance degradation, and reduce the maintenance frequency and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0060] Figure 1 It is a schematic diagram of the technical process of the liquid cooling pipeline temperature control system based on liquid flow rate adjustment provided by the present invention;

[0061] Figure 2 It is a schematic diagram of the framework of the liquid cooling pipeline temperature control system based on liquid flow rate adjustment provided by the present invention;

[0062] Figure 3 It is a schematic diagram of the working process of the liquid cooling pipeline temperature control based on liquid flow rate adjustment provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The following will clearly and completely describe the technical solutions of the present invention in combination with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0064] Please refer to Figures 1 - 3, an embodiment of the first aspect of the present invention provides a liquid-cooled pipeline temperature control system based on liquid flow regulation, comprising:

[0065] A data acquisition module: used to collect the cell temperature, cell internal resistance, and current in real time, and calculate the heat generation of the cell;

[0066] A region division module: used to obtain the weight coefficients of several cells based on the average temperature of the battery module and the heat generation of the cells, and use the clustering algorithm to divide regions according to the weight coefficients and the spatial coordinates of the cells to obtain several regions;

[0067] An optimization control module: used to construct a region optimization model based on the target temperature, solve the flow rate setting values of several regions based on the constraint conditions, and control the liquid-cooling valve using the flow rate setting values;

[0068] A flow feedback module: used to calculate the pressure compensation flow rate according to the pipeline shape and the current flow rate of several regions, and calculate the aging compensation flow rate according to the cell internal resistance, and use the pressure compensation flow rate and the aging compensation flow rate to update the flow rate setting value again.

[0069] It should be noted that the data acquisition module, region division module, optimization control module, and flow feedback module of the present invention are in communication connection.

[0070] Based on the above technical modules, the present invention can effectively solve the problems of uneven flow rate, uneven cooling, and difficulty in reducing the temperature to the control value within the specified time in the prior art, and realizes the precise control of the temperature of the liquid-cooled pipeline. By calculating the heat generation of the cells, reasonably dividing regions, optimizing the flow rate setting value, and dynamically compensating the flow rate and other measures, the heat dissipation efficiency and temperature uniformity of the system are improved, which helps to extend the service life of the battery and improve the battery performance.

[0071] In order to achieve the precise control of the temperature of the liquid-cooled pipeline, in the data acquisition module of this system, a multi-sensor synchronous acquisition technology is adopted to obtain the cell operation parameters in real time. The specific working process is as follows:

[0072] Temperature sensors (accuracy ±0.1°C), internal resistance measurement modules (resolution 0.1 mΩ), and Hall current sensors (response time <1 ms) are arranged at key positions of the battery module to form a three-dimensional data acquisition network; synchronously collect the cell surface temperature T i , dynamic internal resistance R i and charge and discharge current I at a period of 20 ms, and eliminate high-frequency noise through the Kalman filtering algorithm;

[0073] At the same time, thermodynamic tests are pre-conducted on each cell through DSC (differential scanning calorimetry), and a mapping table of the entropy change coefficient ΔS and SOC (state of charge) is established and stored in the system parameter library;

[0074] Then, dynamically look up the table to obtain the ΔS value corresponding to the current SOC, and calculate the heat generation of the battery cell according to the formula Q heat,i = I 2 R i + T i ΔS i . Among them, ΔS is corrected in real time by using the third-order spline interpolation algorithm;

[0075] Establish a circular buffer to store the recent several groups of historical data, and synchronously transmit the original data and calculation results to the dynamic area module through the CAN bus at a rate of 100 Mbps to provide a data basis for area division.

[0076] Then, in the area division module, based on the battery cell temperature T i , heat generation Q heat,i and spatial coordinates (x i , y i ) transmitted in real time by the data acquisition module, combined with the physical layout parameters of the battery module, perform dynamic area division;

[0077] Specifically, first calculate the weight coefficient of each battery cell in real time based on the formula . Among them, α1 and α2 are temperature-heat generation coupling coefficients (in this embodiment, the values determined by the orthogonal experiment method can be 0.6 and 0.4), and ΔT max represents the maximum allowable temperature difference of the battery cell module, and takes the system safety threshold (usually set to ±5°C);

[0078] Then, read the CAD model of the battery module, and define the battery cells with Euclidean distance d ij ≤20 mm as adjacent nodes to establish a three-dimensional spatial adjacency list:

[0079] Then, combine the weight coefficient and spatial position to calculate the node similarity, and its calculation formula is: Sim(i,j) = 1 - |w i - w j | / (w max - w min );

[0080] Construct the Laplacian matrix L = D - S, and perform eigenvalue decomposition on L to obtain the eigenvectors corresponding to the first k smallest eigenvalues to form a k-dimensional feature space; among them, D represents the degree matrix, and D ii = ∑ j S ij , S ij = Sim(i,j);

[0081] Finally, use the improved K-means algorithm to cluster the k-dimensional feature space, and output the area division result {C1, C2,..., C m} and the feature parameter set of each area.

[0082] In the process of achieving precise liquid cooling temperature control, regional division plays a crucial role. When the battery module is charging and discharging, due to factors such as current distribution, contact resistance, and heat dissipation conditions, the heat generation of the battery cells is not uniform in space. If only the traditional global current sharing control method is adopted, it is difficult to effectively solve the local hot spot problem. Moreover, the working conditions during vehicle driving are complex and changeable, which makes the heat generation rate of the battery cells constantly change, and a mechanism that can respond quickly is needed. In addition, there are differences in the aging degree of the battery cells in different regions, and the internal resistance will also be different, so targeted flow adjustment needs to be carried out based on real-time internal resistance data.

[0083] Therefore, by using the clustering algorithm, the battery cells with similar thermal characteristics are divided into the same region, which can significantly improve the temperature uniformity. When allocating the flow rate, factors related to temperature and pipeline length can be comprehensively considered to make the distribution of the coolant more reasonable and improve its usage efficiency. At the same time, regional division speeds up the response speed of the system and can respond more quickly to changes in working conditions. For the aging conditions of the battery cells in different regions, differential flow compensation can also be implemented, which helps to optimize the aging management of the battery.

[0084] When the system receives the temperature control signal, the optimization control module realizes refined flow regulation through the following process:

[0085] First, based on the average temperature of each region at present and the historical flow data Q j,avg , the regional optimization model is constructed as:

[0086] ;

[0087] Among them, T target represents the target temperature, represents the average battery cell temperature of the adjacent region k, η represents the temperature balance coefficient between regions, which is obtained through experimental calibration; represents the flow rate setting values of several regions, which are the solution results of the optimization model; Ν(j) represents the set of adjacent regions of region j, which is determined according to the pipeline topology connection relationship; t represents time, and H represents the preset period;

[0088] In the regional optimization model, the temperature weight reflects the influence of the pipeline length on the heat dissipation efficiency - a longer pipeline requires a higher flow rate weight to compensate for the frictional loss along the way; the flow rate weight achieves dynamic fluctuation suppression by real-time monitoring of the temperature change rate; the inter-regional balance term ensures that the temperature difference between adjacent regions does not exceed the safety threshold through the experimentally calibrated coefficient η, avoiding the battery performance decay caused by local overcooling / overheating;

[0089] It should be noted that in the temperature weight and flow weight, C j represents region j, and L j represents the total pipeline length of region j, and L max represents the maximum value of the pipeline lengths in all regions, and λ represents the global flow fluctuation penalty coefficient, which is obtained through experimental calibration;

[0090] Next, the predicted model is used to calculate the temperature change in the future H steps:

[0091] where Q cool,j (t) represents the cooling power of the region, M j represents the total mass of the battery cells in the region, c p,j represents the equivalent specific heat capacity of the marked region, R jk represents the thermal resistance between region j and adjacent region k, and Δt represents the time difference;

[0092] By setting the prediction period, the temperature change is predicted in advance, providing a basis for pre-adjusting the flow rate and avoiding temperature overshoot caused by control delay;

[0093] Then, the predicted temperature is substituted into the regional optimization model, and the flow rate setting value is determined by solving the quadratic programming problem with constraints The constraint conditions include:

[0094] 1. Regional flow boundary: Q j,min ≤Q j ≤Q j,max , preventing valve saturation;

[0095] 2. Global flow limit: Ensuring the safe operation of the system;

[0096] 3. Adjacent flow difference: |Q j -Q k |≤ΔQ max , avoiding pressure drop imbalance caused by sudden change of local flow velocity;

[0097] where Q j,min and Q j,max respectively represent the minimum and maximum allowable flow rates of the pipeline in region j, Q pump_max represents the maximum output flow rate of the pump, and ΔQ max represents the maximum allowable flow difference between adjacent regions;

[0098] During the solution process, the rolling horizon control strategy is adopted, and the optimal solution is updated every period. Finally, through the feedforward compensation and feedback regulation of the PID controller, the valve opening is adjusted to accurately track the flow rate setting value:

[0099] Among them, K P,j , K I,j , K D,j respectively represent the proportionality coefficient, integral coefficient, and differential coefficient of region j, e j (t) represents the region flow deviation, and Q j (t) represents the real-time flow value of region j, which is obtained by a flow sensor.

[0100] In this embodiment, the optimization control module realizes a good match between coolant distribution and heat dissipation requirements through the decoupling design of temperature weight and flow weight, and can effectively reduce the maximum temperature even under extreme working conditions, ensuring that the battery operates within a suitable temperature range. Multiple constraint conditions ensure that the system operates within the safety threshold, reduce the oscillation amplitude of the global flow, and avoid risks such as pump overload caused by sudden flow changes. By introducing historical flow data, a flow smoothing mechanism is formed during the optimization process, reducing the impact of high-frequency regulation on the valve life. The multi-step ahead temperature prediction enables the system to have a pre-judgment ability, and when the load changes, it can make the temperature reach a stable state faster, realizing the collaborative optimization of temperature uniformity and system efficiency, and providing a reliable and economical thermal management solution for the battery pack.

[0101] Finally, in the flow feedback module, through the real-time collected flow data and internal resistance information, a dual feedback compensation mechanism is formed, and then the compensated flow is used to adjust the system flow set value to ensure that the flow distribution of the entire liquid cooling pipeline system is more reasonable, so as to better meet the heat dissipation requirements of the battery cells in different regions and maintain the system temperature stable.

[0102] Specifically, when calculating the pressure compensation flow, first, the regional pressure drop correction coefficient α j is set according to the pipeline elbows and roughness of several regions. The more pipeline elbows and the greater the roughness, the greater the resistance to the fluid, and the corresponding correction coefficient is also greater;

[0103] Then, according to the valve opening A j (t), the equivalent cross-sectional area S j of the regional valve is calculated, and its calculation formula is S j = A jmax × (A j (t) / 100), where A jmax represents the maximum cross-sectional area when the valve is fully open;

[0104] On this basis, according to the formula , the pressure compensation flow ΔQ P,j is calculated; where ρ represents the coolant density, D h,j represents the hydraulic diameter of region j, and D h,j = 4 × A flow,j / Pwet,j , obtained through geometric calculation (A flow is the cross-sectional area, P wet,j is the wetted perimeter), ΔP j represents the pressure loss, and ΔP d represents the upper limit of the allowable pressure of the system;

[0105] In calculating the aging compensation flow rate, first, the average internal resistance of the battery cells in region j is statistically obtained and the initial average internal resistance of the battery cells is R j,new . As the battery is used, the internal resistance of the battery cells will change. By comparing the average internal resistance of the battery cells with the initial value, the aging degree of the battery cells can be judged;

[0106] Then, according to the formula the aging compensation flow rate ΔQ is calculated R,j . Here, γ j represents the aging compensation intensity coefficient of region j, which is obtained through experimental calibration, and Q j,base represents the set value of the base flow rate of region j;

[0107] Finally, the set flow rate of region j, the pressure compensation flow rate ΔQ P,j and the aging compensation flow rate ΔQ R,j are summed up to obtain the updated flow rate;

[0108] To ensure the safe and stable operation of the system, the minimum value of the updated flow rate and the maximum allowable flow rate Q j,max of region j is taken as the final set flow rate value after updating, enabling the system to dynamically adjust the flow rate according to the actual situation, improving the control accuracy and reliability of the liquid-cooling pipeline temperature control system.

[0109] In the flow rate optimization module, by real-time monitoring of the valve opening and pipeline parameters, the flow rate deviation caused by changes in hydrodynamic characteristics (such as elbow resistance, roughness) is dynamically compensated to ensure the consistency between the theoretical flow rate and the actual flow rate; at the same time, the internal resistance change is used to reflect the battery aging state, and the capacity decay is delayed through differential flow rate compensation, forming a complete closed loop of "flow rate setting - execution - feedback - correction";

[0110] Among them, the physical field feedback eliminates the influence of pipeline physical property changes on the flow rate, and the electrochemical feedback incorporates the battery health state into the control variables, which can improve the accuracy of flow rate distribution. In addition, the compensated flow rate is limited within the safety threshold through safety redundancy design to avoid system risks caused by overcompensation, providing guarantee for the long-term stable operation of the system.

[0111] The following is a specific example of using the liquid-cooling pipeline temperature control system based on liquid flow rate regulation for battery thermal management:

[0112] Suppose a battery system installed in an electric vehicle uses 100 ternary lithium batteries of the 21700 type, grouped in a 10-series and 10-parallel configuration. The vehicle is in a high-temperature environment of 35 °C and is undergoing 1C fast charging (charging current I = 217 A). The battery system utilizes a liquid-cooled pipeline temperature control system based on liquid flow regulation to control the battery temperature, ensuring battery performance and lifespan. Its workflow may include:

[0113] 1. Data acquisition module:

[0114] Calculate the heat generation of each battery cell: Taking the 5th battery cell as an example, its initial temperature T5 = 32 °C, internal resistance R5 = 20 mΩ = 0.02 Ω, and entropy change coefficient ΔS5 = 0.12 J / (K·Ah) (at SOC = 80%); according to the heat generation calculation formula Q heat,i =I 2 R i +T i ΔS i , we can obtain:

[0115] Q heat,5 =(217) 2 ×0.02 + 32×0.12 = 943.18 + 3.84 = 947.02 W;

[0116] Statistically analyze the heat generation of 100 battery cells to obtain the total heat generation ∑Q heat,i =78950 W; These data are collected in real-time through sensors and transmitted to the region division module via the CAN bus every 20 ms.

[0117] 2. Region division module:

[0118] Weight coefficient calculation: For the 5th battery cell, the average temperature T avg =29 °C, the maximum allowable temperature difference ΔT max =5 °C, the temperature weight coefficient α1 = 0.6, and the heat generation weight coefficient α2 = 0.4. According to the weight coefficient calculation formula we can obtain:

[0119] w5 = 0.6×|32 - 29| / 5 + 0.4×(947.02 / 78950) = 0.36 + 0.4×0.012 = 0.3648;

[0120] Region division: Then, through the Laplace matrix eigenvalue decomposition and K-means clustering algorithm, the 100 battery cells are divided into 3 regions. The specific information is as follows:

[0121] Region Number of cells Average temperature (°C) Average heat generation (W) Initial average internal resistance (mΩ) 1 20 31 950 18 2 60 28 700 15 3 20 26 500 14

[0122] 3. Optimization control module:

[0123] The constructed regional optimization model is as follows: The objective function is

[0124]

[0125] Assume that the pipeline lengths of all regions are the same, then the temperature weight w T,j = 1; the flow weight w Q,j = 0.15; the inter-regional balance coefficient η = 0.2;

[0126] Set the constraint conditions as follows:

[0127] Regional flow: Q j ∈[1,5] L / min;

[0128] Global total flow: ∑Qj ≤ 15 L / min;

[0129] Adjacent flow difference: |Qj - Qk| ≤ 1 L / min;

[0130] By solving the quadratic programming, the flow set values of each region are obtained as Q1 * = 4.5 L / min, Q2 * = 3.5 L / min, Q3 * = 2.5 L / min. The total flow is 4.5 + 3.5 + 2.5 = 10.5 L / min < 15 L / min, meeting the constraint conditions;

[0131] 4. Flow feedback module:

[0132] Pressure-compensated flow: Taking region 3 as an example, its flow Q1(t) = 2.5 L / min = 2.5 / (60×1000) m 3 / s ≈ 4.17×10 -5 m 3 / s, the valve opening A3(t) = 30%, the equivalent cross-sectional area S3 = 0.001 m 2 ×0.3 = 0.0003 m 2 The pipeline length L3 = 2 m, the hydraulic diameter D h,3 = 10 mm = 0.01 m, the pressure compensation coefficient α3 = 1.2, the coolant density ρ = 1000 kg / m 3 . According to the pressure-compensated flow calculation formula It can be obtained that:

[0133] ΔP3 = 1.2×(1000 / 2)×( / 0.0003) 2 ×(2 / 0.01) = 600×0.019321×200 = 2318.52 Pa;

[0135] ΔQ P,3= 2.5 × √(1000 / 2318.52) ≈ 1.65 L / min.

[0136] Aging compensation flow: The average internal resistance of Region 3 Initial average internal resistance R 3,new = 14 mΩ, base flow Q 3,base = 2.5 L / min, aging compensation coefficient γ3 = 0.8. According to the aging compensation flow calculation formula It can be obtained that:

[0137] ΔQ R,3 = 0.8 × 2.5 × (16 - 14) / 14 ≈ 0.286 L / min;

[0138] Final flow update: The final flow Q3 of Region 3 final = min(2.5 + 1.65 + 0.286, 5) = 4.436 L / min < 5 L / min.

[0139] Through dynamic region division, optimized flow distribution, and real-time compensation mechanism, the system significantly reduces the temperature difference of the battery module, adapts to the changes in pipeline resistance and cell aging, effectively improves temperature uniformity, response speed, and energy consumption efficiency, and enhances the reliability and adaptability of the system.

[0140] Some of the data in the above formula are taken as numerical values after removing the dimension. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the actual situation; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0141] The working principle of the present invention:

[0142] The data acquisition module collects cell-related data through sensors, processes and calculates it, and then transmits it to the region division module. The region division module divides regions based on this data and passes the results to the optimization control module. The optimization control module constructs a model and solves for the flow set value, and then the flow feedback module performs compensation calculations according to the pipeline and cell conditions to obtain the final flow to control the liquid cooling valve. The liquid cooling valve controls the valve opening according to the final flow and feeds back the real-time flow to the data acquisition module to form a complete closed-loop control system to achieve precise control of the liquid cooling pipeline temperature.

[0143] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A liquid-cooled pipeline temperature control system based on liquid flow regulation, characterized in that, Including: Data acquisition module: used to collect the cell temperature, cell internal resistance, and current in real time, and calculate the heat generation of the cell; Region division module: used to obtain the weight coefficients of several cells based on the average temperature of the battery module and the heat generation of the cell, and use the clustering algorithm to divide regions according to the weight coefficients and the spatial coordinates of the cells to obtain several regions; Optimization control module: used to construct a region optimization model according to the target temperature, solve the flow rate set values of several regions based on the constraint conditions, and control the liquid cooling valve using the flow rate set values; Flow feedback module: used to calculate the pressure compensation flow rate according to the pipeline shape and the current flow rate of several regions, and calculate the aging compensation flow rate according to the cell internal resistance, and use the pressure compensation flow rate and the aging compensation flow rate to update the flow rate set value again.

2. The liquid-cooled pipeline temperature control system based on liquid flow regulation according to claim 1, wherein The calculation formula for the heat generation of the battery cell is: Q heat,i = I 2 R i + T i ΔS i ; where Q heat,i represents the heat generation of the i-th battery cell, I represents the current, R i represents the internal resistance of the i-th battery cell, T i represents the temperature of the i-th battery cell, and ΔS i represents the entropy change coefficient of the i-th battery cell.

3. A liquid-cooling pipeline temperature control system based on liquid flow regulation according to claim 2, characterized in that, The obtaining of the weight coefficients of several cells based on the average temperature of the battery module and the heat generation of the cell includes: Calculate the average temperature T of the battery cell module in real time avg ; According to the formula the weight coefficient w of the i-th battery cell is calculated i ; where ΔT max represents the maximum allowable temperature difference of the battery cell module, α1 represents the weight of the temperature deviation, α2 represents the weight of the heat load ratio, and α1 + α2 = 1.

4. The liquid cooling pipeline temperature control system based on liquid flow regulation according to claim 3, wherein, The division of regions using the clustering algorithm according to the weight coefficients and the spatial coordinates of the cells includes: According to the CAD drawing of the battery module or the installation layout drawing of the battery module, obtain the spatial coordinates of the battery cells, and obtain the spatial coordinates of the battery cells (x i , y i ); Generating an adjacency list according to the physical layout of the cells and defining the relationship between adjacent cells; Calculate the similarity Sim(i,j) of the relationship between adjacent battery cells to obtain the similarity matrix S. The calculation formula is: Sim(i,j) = 1 - |w i - w j | / (w max - w min ); where w max and w min represent the maximum and minimum values of the weight coefficients respectively, and i and j represent the battery cell indices; Calculate the Laplacian matrix according to the similarity matrix: L = D - S; where D represents the degree matrix, and D ii = ∑ j S ij , S ij = Sim(i, j); Performing eigenvalue decomposition on the Laplacian matrix L, and taking the eigenvectors corresponding to the first k smallest eigenvalues to obtain several eigenvectors; Clustering several eigenvectors based on the K-means algorithm to obtain several regions.

5. A liquid-cooled pipeline temperature control system based on liquid flow rate regulation according to claim 1, characterized in that The construction of the region optimization model according to the target temperature includes: Calculate the average temperature of the battery cells in region j And calculate according to the formula to obtain the temperature weight w of region j T,j and the flow weight w Q,j ; where C j represents region j, L j represents the total pipeline length of region j, L max represents the maximum value of the pipeline lengths in all regions, λ represents the global flow fluctuation penalty coefficient, obtained by experimental calibration, Q heat,i represents the heat generation of battery cell i in region j; Determining the set Ν(j) of adjacent regions of region j according to the pipeline topological connection relationship; Use a sliding event window to statistically calculate the average traffic volume of area j within a historical preset time period to obtain the historical average traffic volume Q j,avg ; Constructing the region optimization model as: ； Among them, T target represents the target temperature, represents the average cell temperature of the adjacent region k, and η represents the temperature balance coefficient between regions, which is obtained through experimental calibration. represents the flow rate setting values of several regions, which are the solution results of the optimization model. t represents time, and H represents the preset period.

6. The liquid-cooled pipeline temperature control system based on liquid flow regulation according to claim 5, wherein The solving of the flow rate set values of several regions based on the constraint conditions includes: Constructing a prediction model based on the model predictive control theory as: Among them, Q cool,j (t) represents the regional cooling power, M j represents the total mass of the regional battery cells, c p,j represents the regional equivalent specific heat capacity, R jk represents the thermal resistance between region j and adjacent region k, and Δt represents the time difference; Predict the average temperature change in each region within a preset period H using a prediction model to obtain the average predicted temperature of region j The average predicted temperature of region j is input into the region optimization model, and flow rate set values of several regions are obtained by solving according to the constraint conditions wherein, the constraint conditions include: Regional flow constraint: Q j,min ≤Q j ≤Q j,max ; where Q j,min and Q j,max represent the minimum and maximum allowable flows in the pipeline within region j, respectively; Global total flow constraint: Among them, Q pump_max represents the maximum output flow of the pump; Adjacent area flow difference constraint: |Q j -Q k | ≤ ΔQ max ; where ΔQ max represents the maximum allowable flow difference between adjacent areas.

7. The liquid cooling pipeline temperature control system based on liquid flow regulation according to claim 6, characterized in that, The control of the liquid cooling valve using the flow rate set values includes: The PID control equation is constructed as follows: where K P,j , K I,j , and K D,j represent the proportional coefficient, integral coefficient, and differential coefficient of region j, respectively, e j (t) represents the regional flow deviation, and Q j (t) represents the real-time flow value of region j, which is obtained through a flow sensor; Control the valve opening A of the liquid cooling valve using the PID control equation j (t).

8. A liquid-cooled pipeline temperature control system based on liquid flow regulation according to claim 1, wherein, The calculation of the pressure compensation flow rate according to the pipeline shape and the current flow rate of several regions includes: Set the regional pressure drop correction coefficient α according to the pipe elbows and roughness in several regions j ; According to the valve opening A j (t), the equivalent cross-sectional area S of the regional valve is calculated j , and the calculation formula is: S j = A jmax × (A j (t) / 100); where A jmax represents the maximum cross-sectional area when the valve is fully open; According to the formula the pressure compensation flow rate ΔQ is calculated P,j ; where ρ represents the coolant density, D h,j represents the hydraulic diameter of region j, ΔP j represents the pressure loss, and ΔP d represents the upper limit of the allowable pressure of the system; Q j (t) represents the real-time flow rate value of region j, and t represents time.

9. A liquid-cooled pipeline temperature control system based on liquid flow regulation according to claim 8, characterized in that, The calculation of the aging compensation flow rate according to the cell internal resistance includes: Statistical average internal resistance of the battery cells in region j and the initial average internal resistance R j,new ; According to the formula the aging compensation flow rate ΔQ is calculated R,j ; where γ j represents the aging compensation intensity coefficient of region j, which is obtained through experimental calibration, and Q j,base represents the basic flow rate setting value of region j, which is obtained by solving the regional optimization model.

10. A liquid-cooled pipeline temperature control system based on liquid flow rate regulation according to claim 9, characterized in that, The updating of the flow rate set value again using the pressure compensation flow rate and the aging compensation flow rate includes: Sum the flow rate set value of region j with the pressure compensation flow rate ΔQ P,j and the aging compensation flow rate ΔQ R,j to obtain the updated flow rate; Take the minimum value of the update traffic and the maximum allowable traffic Q of area j as the final set value after update. j,max ​

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